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Although European countries have centralized healthcare systems, patient data is distributed across many different databases. In leveraging AI to improve and tailor treatments, tech companies struggle with getting training data for their algorithms. Drawing from the experience of three startups, this article describes a five-step process for consolidating the data.
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When it comes to leveraging AI, more than 80% of European pharma executives believe that Europe is behind the curve in general — and that European pharma is further behind than most other big-data industries. These are among the findings of a recent survey INSEAD conducted in partnership with Early Metrics (a European startup-rating agency) and Agalio, a Paris-based consulting firm.
Many observers blame Europe’s tough laws regarding data privacy, which are more stringent than those in the United States and China. But our survey suggests that regulation isn’t the problem. Rather, it’s a disconnect between private and public health systems, which makes it difficult for AI programmers to access enough data with which to train their algorithms.
In many European countries, clinical data is kept by individual hospitals and clinics despite the existence of centralized public health systems. That wouldn’t …
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